Many services and applications depend on the detection and classification of motion states of a mobile device and by extension, the motion states of users of the mobile device. For example, a navigation application on a smartphone may switch from pedestrian navigation to vehicular navigation when the smartphone detects a transition of motion states from walking to driving; an application may warn a user to “stop texting while driving” when the smartphone detects that the user is moving at a vehicle speed when texting. Other services relying on a determination of motion states of users through mobile devices include geo-fencing, place-of-reference services, services to improve WiFi connectivity, etc. For these contextual-awareness services and applications, the motion states of interest may include stationary, fidgeting, walking, running, driving, and others.
Conventionally, accelerometers are used in smartphones to produce acceleration signals that are processed by motion classifiers to detect one of the motion states. Accelerometers offer the advantage of low power consumption when compared to other sensors like gyroscopes, an important consideration for mobile devices with their limited battery power. For this reason, most smartphones today are equipped with accelerometers.
While motion states such as stationary, fidget, walk, and run produce acceleration signals that have unique signatures and thus may be processed to detect the correct motion state, detecting the drive state has proven to be more challenging. This is because accelerometer signals produced when smartphones are moving at vehicle speed may be similar to the accelerometer signals produced when the smartphones are moving at pedestrian speed, or when the smartphones are in a pocket, or held in the hand of a stationary user. Due to the similarities in the signatures of the acceleration signals, motion classifiers may misidentify a user as driving when the user is walking or stationary, or falsely identify the user as stationary though the user is driving. These misidentifications are undesirable as they adversely affect applications whose performance relies on the correct identification of the motion states. For example, a high rate of false positives of the drive state when a user is walking may mean unnecessary GPS fixes, resulting in a quicker battery drain. A high rate of false positives of the drive state may also produce erroneous warnings to a user to “stop texting while driving” when the user is actually stationary. While GPS may be leveraged to resolve the motion state ambiguities, enabling GPS causes significant drain on the battery. Furthermore, GPS signals are not always available. Other remedies such as adding additional sensors increase cost and raise power consumption of mobile devices. As such, there is a need for a low power solution to improve the detection of drive state in mobile devices.